Medical applications are increasingly considering the usage of machine learning (ML) models. However, researchers have found that ML models may perform disproportionately poorly on marginalized groups [1]}, [2]}, [3]}. Biases in training data resulting from spurious correlations between the inputs and outputs have received much attention (i.e., “shortcuts" [4]}, [5]}). However, biases can also arise in the labels used for model training. [2]} highlighted one such type of label bias in equating healthcare need with healthcare cost led to downstream inequities in clinical care. We hypothesize that label bias may arise from other sources as well. Specifically, many researchers rely on assumptions about labels that could exacerbate pre-existing disparities in healthcare delivery.
